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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yuchang Cheng Asahara, M. Matsumoto, Y. |
| Copyright Year | 2007 |
| Description | Author affiliation: Nara Inst. of Sci. & Technol., Nara (Yuchang Cheng; Asahara, M.; Matsumoto, Y.) |
| Abstract | This paper describes an annotation guideline for a temporal relation tagged corpus. Our goal is to construct a machine learnable model that automatically analyzes temporal events and relations between events. Since analyzing all combinations of events is inefficient, we examine use of dependency structure analysis to efficiently recognize meaningful temporal relations. We survey a small tagged data set to investigate the coverage of our method. Although the coverage of our methods is about 49%, we find that the dependency structure appears useful for reducing manual efforts in constructing a tagged corpus with temporal relations. |
| Starting Page | 59 |
| Ending Page | 69 |
| File Size | 257016 |
| Page Count | 11 |
| File Format | |
| ISBN | 9780769528366 |
| ISSN | 15301311 |
| DOI | 10.1109/TIME.2007.47 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-06-28 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Guidelines Data mining Artificial intelligence Information analysis Ontologies Machine learning Information processing Signal resolution |
| Content Type | Text |
| Resource Type | Article |
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